Top-Down Scene Discrimination for GAN-Based Driving Simulation
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Solution Overview
Problem
Current autonomous driving systems face challenges in fully testing and training due to the need for a wide range of driving situations and environments, which can be costly and inefficient when relying on physical sensors to capture diverse scenarios.
Innovation Solution
A generative adversarial network (GAN) component generates top-down scene data based on input data and map information, allowing for the creation of realistic and varied simulation environments that can include unusual conditions, such as low light or multiple obstacles, without the need for actual data capture, using a discriminator to improve the realism of generated scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If physical sensors are used to capture diverse driving scenarios for training autonomous driving systems, then the variety and realism of training data are improved, but the cost and time required for data collection increase significantly
Solution Approach 1:
The patent uses a generator network to create synthetic top-down scene images that copy the essential characteristics of real driving scenarios without requiring physical data collection. The generator produces realistic scene representations including vehicles, pedestrians, and environmental features that can be used for training autonomous driving systems, thereby eliminating the time-consuming process of capturing diverse real-world scenarios with physical sensors
Solution Approach 2:
The patent introduces a discriminator network as an intermediary between the generator and the training process. The discriminator evaluates the realism of generated scenes and provides feedback to improve their quality. This intermediary mechanism ensures that synthetic data maintains high fidelity to real scenarios while still being generated efficiently without physical data collection
2Adaptability or versatility
If physical sensors are used to capture diverse driving scenarios for training autonomous driving systems, then the variety and realism of training data are improved, but the cost and time required for data collection increase significantly
Solution Approach 1:
The generator network creates synthetic top-down scene images that replicate real driving scenarios, eliminating the need for expensive physical data collection campaigns. The system generates diverse scenes including various vehicles, pedestrians, road conditions, and environmental features at minimal computational cost compared to deploying sensor arrays in numerous real-world scenarios
Solution Approach 2:
The patent employs computationally efficient generation of synthetic scene data that can be produced in large quantities without the high costs associated with physical data collection. The synthetic scenes are generated on-demand and can be discarded or regenerated as needed, replacing the expensive and resource-intensive process of capturing and storing large datasets from physical sensors
3Productivity
If a generator component creates top-down scene data without physical data capture, then efficiency and cost-effectiveness are improved, but the realism and integrity of generated scenes may deteriorate
Solution Approach 1:
The discriminator network provides continuous feedback to the generator by evaluating whether generated scenes appear real or synthetic. This feedback loop allows the generator to iteratively improve the realism and integrity of generated scenes, ensuring they maintain high quality suitable for training autonomous driving systems while still achieving efficient productivity through synthetic generation
Solution Approach 2:
The generator and discriminator networks are trained dynamically together in an adversarial process where both networks continuously improve. The generator learns to create more realistic scenes in response to the discriminator's evaluation, while the discriminator learns to better detect synthetic scenes. This dynamic interaction ensures the generated scenes achieve sufficient realism for reliable training while maintaining high productivity
Data Source
AI summary
Techniques for top-down scene discrimination are discussed. A system receives scene data associated with an environment proximate a vehicle. The scene data is input to a convolutional neural network (CNN) discriminator trained using a generator and a classification of the output of the CNN discriminator. The CNN discriminator generates an indication of whether the scene data is a generated scene or a captured scene. If the scene data is data generated scene, the system generates a caution notification indicating that a current environmental situation is different from any previous situations. Additionally, the caution notification is communicated to at least one of a vehicle system or a remote vehicle monitoring system.